In the book, 40 experts speak, who explain in clear language what AI is, and what questions, challenges and opportunities the technology brings.
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This papers presents some ideas to use so-called software agents as a software representation of a product not only during manufacturing but also during the whole life cycle of the product. Software agents are autonomous entities capable of collecting useful information about products. By their design and capabilities software agents fit well in the concept of ubiquitous computing. We use these agents in our newly developed manufacturing process. This paper discusses further use of agent technology.
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Handboek voor MKB-ers die de eerste stappen willen zetten in het innoveren met AI.Ondernemers zien natuurlijk kansen in de ontwikkeling van nieuwe producten en/of diensten, ondersteund door AI (Artificial Intelligence). Want data blijkt immers ‘het nieuwe goud’ te zijn! Maar hoe doe je dat dan? Waar begin je? Wat zijn de do’s en wat zijn de dont’s?Voor MKB-ondernemingen, die hier vaak geen specialisten voor in huis hebben, zijn dit de relevante vragen. Speciaal voor deze doelgroep is, binnen het KI-AGIL project, het Handboek AI ontwikkeld. Dit Handboek gaat in op deze en meer vragen, en geeft diverse handreikingen (o.a. om de aanpak op een ‘business verantwoorde, agile manier’ te doen), waardoor een MKB-onderneming goed op weg geholpen wordt met AI.
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Recently, the job market for Artificial Intelligence (AI) engineers has exploded. Since the role of AI engineer is relatively new, limited research has been done on the requirements as set by the industry. Moreover, the definition of an AI engineer is less established than for a data scientist or a software engineer. In this study we explore, based on job ads, the requirements from the job market for the position of AI engineer in The Netherlands. We retrieved job ad data between April 2018 and April 2021 from a large job ad database, Jobfeed from TextKernel. The job ads were selected with a process similar to the selection of primary studies in a literature review. We characterize the 367 resulting job ads based on meta-data such as publication date, industry/sector, educational background and job titles. To answer our research questions we have further coded 125 job ads manually. The job tasks of AI engineers are concentrated in five categories: business understanding, data engineering, modeling, software development and operations engineering. Companies ask for AI engineers with different profiles: 1) data science engineer with focus on modeling, 2) AI software engineer with focus on software development , 3) generalist AI engineer with focus on both models and software. Furthermore, we present the tools and technologies mentioned in the selected job ads, and the soft skills. Our research helps to understand the expectations companies have for professionals building AI-enabled systems. Understanding these expectations is crucial both for prospective AI engineers and educational institutions in charge of training those prospective engineers. Our research also helps to better define the profession of AI engineering. We do this by proposing an extended AI engineering life-cycle that includes a business understanding phase.
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As part of the American Society of Civil Engineers E-Newsletter at page 5&6.
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This exploration with ChatGPT underscores two vital lessons for human rights law education. First, the importance of reflective and critical prompting techniques that challenge it to critique its responses. Second, the potential of customizing AI tools like ChatGPT, incorporating diverse scholarly perspectives to foster a more inclusive and comprehensive understanding of human rights. It also shows the promise of using collaborative approaches to build tools that help create pluriversal approaches to the study of human rights law.
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Over the past three years we have built a practice-oriented, bachelor level, educational programme for software engineers to specialize as AI engineers. The experience with this programme and the practical assignments our students execute in industry has given us valuable insights on the profession of AI engineer. In this paper we discuss our programme and the lessons learned for industry and research.
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From the article: The ethics guidelines put forward by the AI High Level Expert Group (AI-HLEG) present a list of seven key requirements that Human-centered, trustworthy AI systems should meet. These guidelines are useful for the evaluation of AI systems, but can be complemented by applied methods and tools for the development of trustworthy AI systems in practice. In this position paper we propose a framework for translating the AI-HLEG ethics guidelines into the specific context within which an AI system operates. This approach aligns well with a set of Agile principles commonly employed in software engineering. http://ceur-ws.org/Vol-2659/
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